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---
language: de
license: mit
tags:
- flair
- token-classification
- sequence-tagger-model
- hetzner
- hetzner-gex44
- hetzner-gpu
base_model: deepset/gbert-base
datasets:
- stefan-it/co-funer
widget:
- text: Wesentliche Tätigkeiten der Compliance-Funktion wurden an die Mercurtainment
    AG , Düsseldorf , ausgelagert .
---

# Fine-tuned Flair Model on CO-Fun NER Dataset

This Flair model was fine-tuned on the
[CO-Fun](https://arxiv.org/abs/2403.15322) NER Dataset using GBERT Base as backbone LM.

## Dataset

The [Company Outsourcing in Fund Prospectuses (CO-Fun) dataset](https://arxiv.org/abs/2403.15322) consists of
948 sentences with 5,969 named entity annotations, including 2,340 Outsourced Services, 2,024 Companies, 1,594 Locations
and 11 Software annotations.

Overall, the following named entities are annotated:

* `Auslagerung` (engl. outsourcing)
* `Unternehmen` (engl. company)
* `Ort` (engl. location)
* `Software`

## Fine-Tuning

The latest [Flair version](https://github.com/flairNLP/flair/tree/42ea3f6854eba04387c38045f160c18bdaac07dc) is used for
fine-tuning.

A hyper-parameter search over the following parameters with 5 different seeds per configuration is performed:

* Batch Sizes: [`8`, `16`]
* Learning Rates: [`3e-05`, `5e-05`]

More details can be found in this [repository](https://github.com/stefan-it/co-funer). All models are fine-tuned on a
[Hetzner GEX44](https://www.hetzner.com/dedicated-rootserver/matrix-gpu/) with an NVIDIA RTX 4000.

## Results

A hyper-parameter search with 5 different seeds per configuration is performed and micro F1-score on development set
is reported:

| Configuration      | Seed 1      | Seed 2         | Seed 3       | Seed 4       | Seed 5       | Average         |
|--------------------|-------------|----------------|--------------|--------------|--------------|-----------------|
| `bs8-e10-lr5e-05`  | [0.9477][1] | [**0.935**][2] | [0.9517][3]  | [0.9443][4]  | [0.9342][5]  | 0.9426 ± 0.0077 |
| `bs16-e10-lr5e-05` | [0.9214][6] | [0.9364][7]    | [0.9334][8]  | [0.9489][9]  | [0.9257][10] | 0.9332 ± 0.0106 |
| `bs8-e10-lr3e-05`  | [0.928][11] | [0.9248][12]   | [0.9421][13] | [0.9295][14] | [0.9263][15] | 0.9301 ± 0.0069 |
| `bs16-e10-lr3e-05` | [0.918][16] | [0.9256][17]   | [0.9331][18] | [0.9273][19] | [0.9196][20] | 0.9247 ± 0.0061 |

[1]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs8-e10-lr5e-05-1
[2]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs8-e10-lr5e-05-2
[3]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs8-e10-lr5e-05-3
[4]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs8-e10-lr5e-05-4
[5]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs8-e10-lr5e-05-5
[6]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs16-e10-lr5e-05-1
[7]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs16-e10-lr5e-05-2
[8]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs16-e10-lr5e-05-3
[9]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs16-e10-lr5e-05-4
[10]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs16-e10-lr5e-05-5
[11]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs8-e10-lr3e-05-1
[12]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs8-e10-lr3e-05-2
[13]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs8-e10-lr3e-05-3
[14]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs8-e10-lr3e-05-4
[15]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs8-e10-lr3e-05-5
[16]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs16-e10-lr3e-05-1
[17]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs16-e10-lr3e-05-2
[18]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs16-e10-lr3e-05-3
[19]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs16-e10-lr3e-05-4
[20]: https://hf.co/stefan-it/flair-co-funer-gbert_base-bs16-e10-lr3e-05-5

The result in bold shows the performance of the current viewed model.

Additionally, the Flair [training log](training.log) and [TensorBoard logs](../../tensorboard) are also uploaded to the model
hub.